
"""simple docstring"""


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_lowercase				 =					16
_lowercase				 =					32






def    _snake_case					(		snake_case__ :  Accelerator						,   snake_case__ :  int = 16  ):
	A     = AutoTokenizer.from_pretrained('bert-base-cased'  )
	A     = load_dataset('glue'						,   'mrpc'  )

	def tokenize_function(snake_case__ :  Optional[Any]  ):
		# max_length=None => use the model max length (it's actually the default)
		A     = tokenizer(examples['sentence1']						,   examples['sentence2']						,   truncation=snake_case__						,   max_length=snake_case__  )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		A     = datasets.map(
		    snake_case__						,   batched=snake_case__						,   remove_columns=['idx', 'sentence1', 'sentence2']						,   )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	A     = tokenized_datasets.rename_column('label'						,   'labels'  )

	def collate_fn(snake_case__ :  int  ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		A     = 128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			A     = 16
		elif accelerator.mixed_precision != "no":
			A     = 8
		else:
			A     = None

		return tokenizer.pad(
		    snake_case__						,   padding='longest'						,   max_length=snake_case__						,   pad_to_multiple_of=snake_case__						,   return_tensors='pt'						,   )

	# Instantiate dataloaders.
	A     = DataLoader(
	    tokenized_datasets['train']						,   shuffle=snake_case__						,   collate_fn=snake_case__						,   batch_size=snake_case__  )
	A     = DataLoader(
	    tokenized_datasets['validation']						,   shuffle=snake_case__						,   collate_fn=snake_case__						,   batch_size=snake_case__  )

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		_lowercase				 =					mocked_dataloaders  # noqa: F811






def    _snake_case					(		snake_case__ :  Union[str, Any]						,   snake_case__ :  List[Any]  ):
	# For testing only
	if os.environ.get('TESTING_MOCKED_DATALOADERS'						,   snake_case__  ) == "1":
		A     = 2
	# Initialize accelerator
	A     = Accelerator(cpu=args.cpu						,   mixed_precision=args.mixed_precision  )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A     = config['lr']
	A     = int(config['num_epochs']  )
	A     = int(config['seed']  )
	A     = int(config['batch_size']  )

	A     = evaluate.load('glue'						,   'mrpc'  )

	# If the batch size is too big we use gradient accumulation
	A     = 1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A     = batch_size // MAX_GPU_BATCH_SIZE
		A     = MAX_GPU_BATCH_SIZE

	set_seed(snake_case__  )
	A ,   A     = get_dataloaders(snake_case__						,   snake_case__  )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A     = AutoModelForSequenceClassification.from_pretrained('bert-base-cased'						,   return_dict=snake_case__  )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	A     = model.to(accelerator.device  )

	# Instantiate optimizer
	A     = AdamW(params=model.parameters()						,   lr=snake_case__  )

	# Instantiate scheduler
	A     = get_linear_schedule_with_warmup(
	    optimizer=snake_case__						,   num_warmup_steps=100						,   num_training_steps=(len(snake_case__  ) * num_epochs) // gradient_accumulation_steps						,   )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	A ,   A ,   A ,   A ,   A     = accelerator.prepare(
	    snake_case__						,   snake_case__						,   snake_case__						,   snake_case__						,   snake_case__  )

	# Now we train the model
	for epoch in range(snake_case__  ):
		model.train()
		for step, batch in enumerate(snake_case__  ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device  )
			A     = model(**snake_case__  )
			A     = outputs.loss
			A     = loss / gradient_accumulation_steps
			accelerator.backward(snake_case__  )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A     = 0
		for step, batch in enumerate(snake_case__  ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device  )
			with torch.no_grad():
				A     = model(**snake_case__  )
			A     = outputs.logits.argmax(dim=-1  )
			A ,   A     = accelerator.gather((predictions, batch['labels'])  )
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(snake_case__  ) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A     = predictions[: len(eval_dataloader.dataset  ) - samples_seen]
					A     = references[: len(eval_dataloader.dataset  ) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=snake_case__						,   references=snake_case__						,   )

		A     = metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'						,   snake_case__  )






def    _snake_case					(		):
	A     = argparse.ArgumentParser(description='Simple example of training script.'  )
	parser.add_argument(
	    '--mixed_precision'						,   type=snake_case__						,   default=snake_case__						,   choices=['no', 'fp16', 'bf16', 'fp8']						,   help='Whether to use mixed precision. Choose'
	    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
	    'and an Nvidia Ampere GPU.'						,   )
	parser.add_argument('--cpu'						,   action='store_true'						,   help='If passed, will train on the CPU.'  )
	A     = parser.parse_args()
	A     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
	training_function(snake_case__						,   snake_case__  )


if __name__ == "__main__":
		main()
'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       bool:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    if len(UpperCamelCase__							) == 1:
        return True
    __lowerCamelCase            =    series[1] - series[0]
    for index in range(len(UpperCamelCase__							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    __lowerCamelCase            =    0
    for val in series:
        answer += val
    return answer / len(UpperCamelCase__							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
